Clustering/Clustering_right_stances.ipynb

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    {
      "cell_type": "markdown",
      "source": [
        "Let's start with clustering all the stances from the right foot only.\n",
        "The stances are calculated from one feature (the sum of all sensors under the right foot for each patient)"
      ],
      "metadata": {
        "id": "6GVq2cKOJlvS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Importing"
      ],
      "metadata": {
        "id": "BhQLOcEniNgp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pip install tslearn"
      ],
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          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: tslearn in /usr/local/lib/python3.8/dist-packages (0.5.3.2)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.8/dist-packages (from tslearn) (1.2.0)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.8/dist-packages (from tslearn) (1.22.4)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.8/dist-packages (from tslearn) (1.7.3)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.8/dist-packages (from tslearn) (1.0.2)\n",
            "Requirement already satisfied: numba in /usr/local/lib/python3.8/dist-packages (from tslearn) (0.56.4)\n",
            "Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.8/dist-packages (from numba->tslearn) (6.0.0)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.8/dist-packages (from numba->tslearn) (57.4.0)\n",
            "Requirement already satisfied: llvmlite<0.40,>=0.39.0dev0 in /usr/local/lib/python3.8/dist-packages (from numba->tslearn) (0.39.1)\n",
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            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.8/dist-packages (from importlib-metadata->numba->tslearn) (3.13.0)\n"
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              "<Figure size 432x288 with 0 Axes>"
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "from sklearn import decomposition\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn import decomposition\n",
        "\n",
        "import os\n",
        "\n",
        "from tqdm.auto import tqdm\n",
        "\n",
        "from tslearn.clustering import TimeSeriesKMeans\n",
        "from tslearn.utils import to_time_series_dataset\n",
        "\n",
        "import ast\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "plt.figure()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Reading the dataset"
      ],
      "metadata": {
        "id": "WmQAJVEjiP4Z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# path of original data folder\n",
        "data_path = \"/content/drive/MyDrive/ITMO-Master's/Thesis/3rd_semester/Data\"\n",
        "csvD_path = \"/content/drive/MyDrive/ITMO-Master's/Thesis/3rd_semester/Data/csv_files\"\n",
        "demographics = \"/content/drive/MyDrive/ITMO-Master's/Thesis/3rd_semester/Data/demographics.csv\"\n",
        "dem_df = pd.read_csv(demographics)"
      ],
      "metadata": {
        "id": "NJh8EXU71npV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# reading the data info file about the right stances\n",
        "right_stances_level_df= pd.read_csv(data_path + '/data_right_stances.csv')"
      ],
      "metadata": {
        "id": "RgCNMtgn3S1C"
      },
      "execution_count": null,
      "outputs": []
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      "cell_type": "code",
      "source": [
        "right_stances_level_df.head()"
      ],
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              "       ID  y  level                                      right_stances\n",
              "0  GaPt03  1    3.0  [23.1, 41.69, 71.17, 138.82, 195.69, 230.78, 2...\n",
              "1  GaPt03  1    3.0  [34.76, 61.27, 113.19, 165.22, 218.02, 260.26,...\n",
              "2  GaPt03  1    3.0  [25.41, 39.38, 55.55, 89.98, 125.95, 168.63, 2...\n",
              "3  GaPt03  1    3.0  [27.72, 46.31, 74.58, 135.85, 189.09, 226.93, ...\n",
              "4  GaPt03  1    3.0  [32.45, 55.55, 113.3, 180.95, 243.87, 285.01, ..."
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      "source": [
        "right_stances_level_df.shape"
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          "base_uri": "https://localhost:8080/"
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          "execution_count": 6
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      "cell_type": "code",
      "source": [
        "len(right_stances_level_df['ID'].unique()) # number of different subjects"
      ],
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              "165"
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          "metadata": {},
          "execution_count": 7
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    {
      "cell_type": "code",
      "source": [
        "right_stances_level_df.index"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m94HVIfDq7LG",
        "outputId": "9407c9c0-58e3-4780-fe8c-4561419f8fe1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RangeIndex(start=0, stop=31951, step=1)"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### The last column is saved as string while it is a list, in the following we transform it to list."
      ],
      "metadata": {
        "id": "QXdKpXoD5HYI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "type(right_stances_level_df[\"right_stances\"][0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "60gWXyDhk6wc",
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          "data": {
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              "str"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from ast import literal_eval\n",
        "right_stances_level_df['right_stances'] =[ast.literal_eval(x) for x in right_stances_level_df['right_stances'] ]\n",
        "# t=df.right_stances.apply(lambda s: list(ast.literal_eval(s)))"
      ],
      "metadata": {
        "id": "x4B7FJQukpaA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "type(right_stances_level_df['right_stances'][0] )"
      ],
      "metadata": {
        "id": "3-YJkXDslzxf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d02209ad-7653-4b85-df9c-e2fea319b60a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "list"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Deleting lists shorter than 2 items"
      ],
      "metadata": {
        "id": "qoy1lhBktQJD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# getting the stances lists\n",
        "stances = right_stances_level_df['right_stances']"
      ],
      "metadata": {
        "id": "SM-oUty9sG8T"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "stances"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eY6oZpIQtWkv",
        "outputId": "95e26719-d518-4e8a-bc4b-d0851d339731"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        [23.1, 41.69, 71.17, 138.82, 195.69, 230.78, 2...\n",
              "1        [34.76, 61.27, 113.19, 165.22, 218.02, 260.26,...\n",
              "2        [25.41, 39.38, 55.55, 89.98, 125.95, 168.63, 2...\n",
              "3        [27.72, 46.31, 74.58, 135.85, 189.09, 226.93, ...\n",
              "4        [32.45, 55.55, 113.3, 180.95, 243.87, 285.01, ...\n",
              "                               ...                        \n",
              "31946    [34.32, 294.47, 378.95, 497.2, 559.68, 614.35,...\n",
              "31947    [78.65, 367.07, 448.8, 551.32, 620.73, 679.14,...\n",
              "31948    [143.99, 355.96, 471.02, 551.32, 627.99, 676.5...\n",
              "31949    [159.17, 374.77, 473.99, 565.51, 661.43, 747.3...\n",
              "31950    [22.55, 63.36, 99.0, 145.2, 205.92, 254.87, 32...\n",
              "Name: right_stances, Length: 31951, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# get the ids of lists that have less than 2 elements\n",
        "indices = [ id for id,x in enumerate(stances) if len(x)<2 ]"
      ],
      "metadata": {
        "id": "BRyYdTq2tqQw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# All the stances #31951\n",
        "right_stances_level_df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yqRzMLPU1Re4",
        "outputId": "eff21241-e766-4152-f70f-ed25b8a56771"
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          "metadata": {},
          "execution_count": 58
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# drop stances lists that have less than 2 elements\n",
        "updated_df= right_stances_level_df.drop(indices)"
      ],
      "metadata": {
        "id": "hBRDgbkI1Khm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Stances with more than 1 vlaue #30595\n",
        "updated_df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WtigbHaA1Tce",
        "outputId": "ee2ebf90-f7af-4eb1-b450-adff5d745f12"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(30595, 4)"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "------------\n"
      ],
      "metadata": {
        "id": "kgcSy73V1Yl2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Checking all the remaining lists are longer than 2"
      ],
      "metadata": {
        "id": "UNJB5YhW1sPl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "stances2 = updated_df['right_stances']"
      ],
      "metadata": {
        "id": "EZbs94bz1aSc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "indices2 = [ id for id,x in enumerate(stances2) if len(x)<2 ]"
      ],
      "metadata": {
        "id": "Rr02mE631dQX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "indices2 # there are no lists shorter than 2 in the updated_df dataframe"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T44kNDqU1kKG",
        "outputId": "76b55d9d-8d39-4de8-d72f-7917b13c2a0b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[]"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "--------------\n"
      ],
      "metadata": {
        "id": "wy5kf2Nd1WpQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Shuffling the dataset"
      ],
      "metadata": {
        "id": "3Rp83sB9jVeT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = updated_df.sample(frac = 1,random_state=4)"
      ],
      "metadata": {
        "id": "R3ZOEC2jM8lT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UD3SmNKgSqCx",
        "outputId": "6cdb6b43-09d5-4fd5-c790-26771d25de61"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(30595, 4)"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "NGU1mJSjSrLE",
        "outputId": "e20944e7-32fe-4510-9d1a-4d804eb4e2bd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           ID  y  level                                      right_stances\n",
              "16712  GaCo17  0    0.0  [25.08, 301.29, 402.49, 517.88, 573.98, 600.16...\n",
              "6497   SiCo01  0    0.0  [27.83, 55.77, 98.12, 156.97, 227.26, 284.57, ...\n",
              "15449  GaCo10  0    0.0  [52.69, 127.93, 186.89, 246.18, 310.09, 403.48...\n",
              "25273  JuPt15  1    2.0  [161.48, 436.26, 572.55, 729.3, 802.34, 842.71...\n",
              "5199   SiPt34  1    1.0  [71.39, 188.21, 327.58, 470.69, 595.54, 681.56..."
            ],
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              "      <th>16712</th>\n",
              "      <td>GaCo17</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>[25.08, 301.29, 402.49, 517.88, 573.98, 600.16...</td>\n",
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              "    <tr>\n",
              "      <th>6497</th>\n",
              "      <td>SiCo01</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>[27.83, 55.77, 98.12, 156.97, 227.26, 284.57, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15449</th>\n",
              "      <td>GaCo10</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>[52.69, 127.93, 186.89, 246.18, 310.09, 403.48...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25273</th>\n",
              "      <td>JuPt15</td>\n",
              "      <td>1</td>\n",
              "      <td>2.0</td>\n",
              "      <td>[161.48, 436.26, 572.55, 729.3, 802.34, 842.71...</td>\n",
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              "    <tr>\n",
              "      <th>5199</th>\n",
              "      <td>SiPt34</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>[71.39, 188.21, 327.58, 470.69, 595.54, 681.56...</td>\n",
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            ]
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Checking AGAIN that all the stances lists have at least 2 items"
      ],
      "metadata": {
        "id": "4uj4-SXVjV0O"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "IDs = [ id for id,x in enumerate(df['right_stances']) if len(x)<2 ]"
      ],
      "metadata": {
        "id": "3FNBpn6tjjWa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(IDs) # the length is 0 then yes all the stances lists have at least 2 items"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oBYWoxOhjw4l",
        "outputId": "10f920a0-6993-4541-9aac-2003c47ad117"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Transforming the dataset to a compitable format for the tslearn library."
      ],
      "metadata": {
        "id": "mtEdv4fD5vU4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = to_time_series_dataset(df['right_stances'].to_list())"
      ],
      "metadata": {
        "id": "OSm0ZSyX4elq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(X)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZPiE6T3uarNh",
        "outputId": "56970311-b092-4a82-f452-baaeec9ddd3b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "30595"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Clustering for only 1000 subjects"
      ],
      "metadata": {
        "id": "4AphDI6H4kGG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "KM_right = TimeSeriesKMeans(n_clusters=4, metric=\"dtw\")\n",
        "clusters_right = KM_right.fit_predict(X[:1000]) # Excuteing time ~ 42 mins"
      ],
      "metadata": {
        "id": "C_31SuoC512s"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Visualizing the clusters"
      ],
      "metadata": {
        "id": "5STlvnxAlJMs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "predicted={0:[], 1:[], 2:[], 3:[]}\n",
        "for i, v in enumerate(clusters_right):\n",
        "    predicted[v].append(i)"
      ],
      "metadata": {
        "id": "0hsyV5JmaweP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for label in predicted[0]:\n",
        "  plt.plot(X[label].ravel(), \"k-\", alpha=.2)\n",
        "  #plt.plot(km.cluster_centers_[0].ravel(), \"r-\")\n",
        "  plt.title(\"Cluster 0\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "7Y1T56dtkz2U",
        "outputId": "7ce1531c-d7c6-4752-cd63-4736ae24d6f2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for label in predicted[1]:\n",
        "  plt.plot(X[label].ravel(), \"k-\", alpha=.2)\n",
        "  #plt.plot(km.cluster_centers_[0].ravel(), \"r-\")\n",
        "  plt.title(\"Cluster 1\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "fon0wtrwlcKL",
        "outputId": "40ed99cd-534b-4c9e-c4e3-f950a1fccd5d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for label in predicted[2]:\n",
        "  plt.plot(X[label].ravel(), \"k-\", alpha=.2)\n",
        "  #plt.plot(km.cluster_centers_[0].ravel(), \"r-\")\n",
        "  plt.title(\"Cluster 2\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "YrFqY5UIlf2N",
        "outputId": "36358ae6-611b-4428-92cb-737443b7e772"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for label in predicted[3]:\n",
        "  plt.plot(X[label].ravel(), \"k-\", alpha=.2)\n",
        "  #plt.plot(km.cluster_centers_[0].ravel(), \"r-\")\n",
        "  plt.title(\"Cluster 3\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "grg9VNN8lhxN",
        "outputId": "fab583ec-e4ae-4383-b4e9-be21b8bdbd60"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Calculate Data Distributions among the clusters"
      ],
      "metadata": {
        "id": "9tu2X2cpl_MN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# the levels of 1000 subjects that were clustered\n",
        "levels = df['level'][:1000]"
      ],
      "metadata": {
        "id": "uNHzvZyVllxc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# creating a datafrmae that contains each time series' cluster with the actual PD_level\n",
        "distriutions = pd.DataFrame(list(zip(clusters_right, levels)),\n",
        "               columns =['cluster', 'PD_level'])"
      ],
      "metadata": {
        "id": "9p_iM7sxmHla"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "distriutions"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "KQOlMRImm0u0",
        "outputId": "39a9d49f-89e0-4fe0-edfd-c49cc4930f1e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     cluster  PD_level\n",
              "0          0       0.0\n",
              "1          2       0.0\n",
              "2          0       0.0\n",
              "3          0       2.0\n",
              "4          2       1.0\n",
              "..       ...       ...\n",
              "995        0       2.0\n",
              "996        0       0.0\n",
              "997        2       2.0\n",
              "998        0       3.0\n",
              "999        0       1.0\n",
              "\n",
              "[1000 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-79c9eec7-0ab6-496e-a6da-ed162f315088\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
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              "      <th>996</th>\n",
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              "      <th>997</th>\n",
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              "      <td>3.0</td>\n",
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              "    <tr>\n",
              "      <th>999</th>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1000 rows × 2 columns</p>\n",
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              "\n",
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              "\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "counts = distriutions.pivot_table(index = ['cluster','PD_level'], aggfunc ='size')"
      ],
      "metadata": {
        "id": "N_LlhJG8oDIm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# the counts pivot table shows the count of subjects of each PD level in each cluster\n",
        "counts"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sTG_o-3ym6_0",
        "outputId": "06a1dfd5-3391-41ba-91cf-5994b3302fd6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "cluster  PD_level\n",
              "0        0.0         239\n",
              "         1.0         272\n",
              "         2.0         195\n",
              "         3.0          64\n",
              "1        0.0           5\n",
              "         1.0           9\n",
              "         2.0          15\n",
              "         3.0           1\n",
              "2        0.0          77\n",
              "         1.0          64\n",
              "         2.0          32\n",
              "         3.0          22\n",
              "3        0.0           1\n",
              "         1.0           2\n",
              "         3.0           2\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculating the number of each PD level in the clustered data\n",
        "PD_0 = counts[0][0.0]+counts[1][0.0]+counts[2][0.0]+counts[3][0.0]\n",
        "PD_1 = counts[0][1.0]+counts[1][1.0]+counts[2][1.0]+counts[3][1.0]\n",
        "PD_2 = counts[0][2.0]+counts[1][2.0]+counts[2][2.0]#+counts[3][2.0]\n",
        "PD_3 = counts[0][3.0]+counts[1][3.0]+counts[2][3.0]+counts[3][3.0]"
      ],
      "metadata": {
        "id": "I7UEvgm-ndwp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# the counts from the pivot table `counts`\n",
        "counts.values"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L0R_S1V-qCCf",
        "outputId": "f2004e5c-f8b3-4fc0-835a-ce6bb8af437c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([239, 272, 195,  64,   5,   9,  15,   1,  77,  64,  32,  22,   1,\n",
              "         2,   2])"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Finding the precentage of each PD level of the count of subjects in each cluster individually \n",
        "each_cluster_distribution = list(counts.values[:4]/len(predicted[0])) # predicted[0] is the count of individuals \n",
        "each_cluster_distribution+=list(counts.values[4:8]/len(predicted[1]))\n",
        "each_cluster_distribution+=list(counts.values[8:12]/len(predicted[2]))\n",
        "each_cluster_distribution+=list(counts.values[12:15]/len(predicted[3]))\n",
        "each_cluster_distribution = list(np.around(np.array(each_cluster_distribution),2))"
      ],
      "metadata": {
        "id": "dnJ7BD_jr8cz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Find the precentage of each PD level distributed among clusters of the count of each PD level in the 1000 subjects in data\n",
        "each_level_distribution=15*[0]\n",
        "for i in range(len(counts.values)-1):\n",
        "  if (i%4 == 0 ):\n",
        "    each_level_distribution[i] = counts.values[i]/PD_0\n",
        "  elif (i%4 == 1 ):\n",
        "    each_level_distribution[i] = counts.values[i]/PD_1\n",
        "  elif (i%4 == 2 ):\n",
        "    each_level_distribution[i] = counts.values[i]/PD_2\n",
        "  elif (i%4 == 3 ):\n",
        "    each_level_distribution[i] = counts.values[i]/PD_3"
      ],
      "metadata": {
        "id": "3ytpnM1jwedL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "each_level_distribution[14] = counts.values[i]/PD_3"
      ],
      "metadata": {
        "id": "S8b8EJzfxcTa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# rounding\n",
        "each_level_distribution = list(np.around(np.array(each_level_distribution),2))"
      ],
      "metadata": {
        "id": "lzafUmyHyInw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# forming the final dataset that hold the counts and distibutions\n",
        "clusters = [0,0,0,0,1,1,1,1,2,2,2,2,3,3,3]\n",
        "levels = list(3* [0.0,1.0,2.0,3.0]) + list([0.0,1.0,3.0])"
      ],
      "metadata": {
        "id": "PCmSSBSDvp2c"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Distributions_df = pd.DataFrame(list(zip(clusters, levels,counts.values, each_cluster_distribution,each_level_distribution)),\n",
        "               columns =['cluster', 'PD_level', 'subjects_count', 'distribution_in_cluster' , 'distribution_in_levels'])"
      ],
      "metadata": {
        "id": "hWCAJ5kByivm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "`distribution_in_cluster` columns holds the precentage of each PD level in each cluster. The sum of it in one cluster = 1"
      ],
      "metadata": {
        "id": "iSmd0kzB7659"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "`distribution_in_levels` column holds the precentage of each PD level of the total count of PD level. The sum of numbers for each pd level = 1"
      ],
      "metadata": {
        "id": "CFpzM9vU8Pyf"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### The distributions"
      ],
      "metadata": {
        "id": "CXmopmiMhvJ8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Distributions_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 520
        },
        "id": "1HGUxIihzO9v",
        "outputId": "adfa7b4d-0a82-4ab8-daad-c6ddfcfa9f72"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    cluster  PD_level  subjects_count  distribution_in_cluster  \\\n",
              "0         0       0.0             239                     0.31   \n",
              "1         0       1.0             272                     0.35   \n",
              "2         0       2.0             195                     0.25   \n",
              "3         0       3.0              64                     0.08   \n",
              "4         1       0.0               5                     0.17   \n",
              "5         1       1.0               9                     0.30   \n",
              "6         1       2.0              15                     0.50   \n",
              "7         1       3.0               1                     0.03   \n",
              "8         2       0.0              77                     0.39   \n",
              "9         2       1.0              64                     0.33   \n",
              "10        2       2.0              32                     0.16   \n",
              "11        2       3.0              22                     0.11   \n",
              "12        3       0.0               1                     0.20   \n",
              "13        3       1.0               2                     0.40   \n",
              "14        3       3.0               2                     0.40   \n",
              "\n",
              "    distribution_in_levels  \n",
              "0                     0.74  \n",
              "1                     0.78  \n",
              "2                     0.81  \n",
              "3                     0.72  \n",
              "4                     0.02  \n",
              "5                     0.03  \n",
              "6                     0.06  \n",
              "7                     0.01  \n",
              "8                     0.24  \n",
              "9                     0.18  \n",
              "10                    0.13  \n",
              "11                    0.25  \n",
              "12                    0.00  \n",
              "13                    0.01  \n",
              "14                    0.02  "
            ],
            "text/html": [
              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>cluster</th>\n",
              "      <th>PD_level</th>\n",
              "      <th>subjects_count</th>\n",
              "      <th>distribution_in_cluster</th>\n",
              "      <th>distribution_in_levels</th>\n",
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              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1</td>\n",
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              "      <td>0.50</td>\n",
              "      <td>0.06</td>\n",
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              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1</td>\n",
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              "      <td>1</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.01</td>\n",
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              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.39</td>\n",
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              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2</td>\n",
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              "      <td>64</td>\n",
              "      <td>0.33</td>\n",
              "      <td>0.18</td>\n",
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              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>32</td>\n",
              "      <td>0.16</td>\n",
              "      <td>0.13</td>\n",
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              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>2</td>\n",
              "      <td>3.0</td>\n",
              "      <td>22</td>\n",
              "      <td>0.11</td>\n",
              "      <td>0.25</td>\n",
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              "    <tr>\n",
              "      <th>12</th>\n",
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              "      <td>1</td>\n",
              "      <td>0.20</td>\n",
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              "    <tr>\n",
              "      <th>13</th>\n",
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              "      <td>0.40</td>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 116
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Cluster 0** has 74% of Health Control, 78% of people with 1.0 Parkinson's level, 81% of people with 2.0 Parkinson's level, and 72% of people with 3 PL.\n",
        "\n"
      ],
      "metadata": {
        "id": "18tMcTEk83bx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "31% of people in **Cluster 0** are from Health Control, 35% people with 1.0 PL, 25%  people with 2.0 PL, and 8% people with 3 PL."
      ],
      "metadata": {
        "id": "NttVwB2e-uPF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Trying again clustering with 2 clusters"
      ],
      "metadata": {
        "id": "f3Ffr6NZ3jtY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "KM_right_2_clusters = TimeSeriesKMeans(n_clusters=2, metric=\"dtw\")\n",
        "clusters_right_2_clusters = KM_right_2_clusters.fit_predict(X[:1000]) "
      ],
      "metadata": {
        "id": "YI3MS1_YzQC-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "clusters_right_2_clusters"
      ],
      "metadata": {
        "id": "hy9JneUZ3wdl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "fwDjTiDXb8ny"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}